As more and more organizations switch to enterprise data storage systems, more and more information has become available for electronic review, analysis, and analytics. In fact, in many industries, programmatic analysis of stored data repositories is a requirement for various audit, regulatory, and certification processes. For example, in the healthcare field, access to federal funds by healthcare organizations (HCOs) may require strict compliance with certain performance measurement and reporting requirements, and contracts with insurers may require that HCOs adhere to certain minimum standards, and performance of physicians within HCOs may be assessed using industry standard measures and customized measures. To derive the data used to meet these requirements, enterprise computing systems may aggregate data from a variety of sources and apply metric calculations against these data sets. However, current products for performing this analysis are inflexible and often tightly coupled to particular data sets. With the move to remote storage and a corresponding increase in diversity among data types and stored content, such products require strict knowledge of input data sets and adherence to a rigid framework for processing of input data. Such products use statically defined metric calculation methods that require a developer to directly modify the source of the product in order to implement a new or alternative metric calculation. Furthermore, such products require manual identification of which metrics to execute against particular sets of input data, such that the system lacks any awareness of whether a given metric applies to a given set of input data absent a hard-coded association. Through applied effort, ingenuity, and innovation, Applicant has solved many of these identified problems by developing a technical solution that is embodied by the present invention, which is described in detail below.